PREDICTORS OF PROLONGED HOSPITAL STAY

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1 PREDICTORS OF PROLONGED HOSPITAL STAY IN CARDIAC SURGERY Zuraida Khairudin Faculty of Science Computer and Mathematics, Universiti Teknologi MARA, Malaysia ABSTRACT quality of life. Many studies have assessed the risk factors associated with length of stay but only a few have discussed the risk factors of prolonged stay after surgery. Therefore, this study is aimed to determine the risk factors affecting long term stay in hospital after CABG and to make comparison between group for each risk factors. All the risk factors were determined by Kaplan-Meier analysis. Overall, 3096 of CABG patients were discharged within less than 14 days, whereas 332 patients required that patients with diabetes and wound infection stayed longer in the hospital ce-bs, FSPU, UiTM. All rights reserved. Keywords: CABG, length of stay, prolonged stay, Kaplan-Meier analysis INTRODUCTION Serious illness such as heart disease is a leading cause of death of people around the world (Mohammad et al., 2008). Duration of stay in the hospitals require high treatment costs. The prolonged stay after cardiac surgery can Many studies have assessed the risk factors associated with length of stay after surgical treatment (Ono et al., 2010 & Nawata et al.,2009) but only a

2 JOURNAL OF ASIAN BEHAVIOURAL STUDIES, VOLUME 2, NUMBER 7, OCTOBER 2012 few have discussed the risk factors related to prolonged stay after surgery (Paul et al., 2000 & Rony et al., 2008). This study is aimed to :a)determine the risk factors affecting long term stay in hospital after CABG surgery, and b) make comparison between group for each risk factors. In order to do so, length of stay in hospital, age, sex, risk factors such as diabetes mellitus, hypertension, obesity, stroke and wound infection must be investigated. LITERATURE REVIEW The study of risk factors that affect the length of stay in hospital and its impact on resource utilization are reviewed by Yaseen et al. (2002).Length of stay of patients in hospital after CABG surgery was analyzed using a logistic regression model (Eric et al., 2002). Multiple regression was used to analyse data for the patients survived surgery for perforated peptic ulcer (Li et al., 2009). Jeroen et al. the risk of ICU stay after cardiac surgery using the Kaplan-Meier curves. Hazards Model. The same method is used by Paul et al. (2000) who study the prolonged stay of patients of mechanical ventilation after CABG surgery. Risk factors and comparisons of stay between sex for demented elderly was analysed using Cox Proportional Hazard Model with two types of scores of patients (Ono et al., 2010). 68 Discrete proportional hazard model was used by Nawata et al. (2009) to study the length of stay for cataract patients in Japan. Rony et al., (2008) cardiac surgery using univariate and multivariate analysis. Longitudinal analysis was conducted to predict prolonged ICU stay due to hypertension following CABG surgery (Julie et al., 2010). In this study, categorical variables was analysed using chi-square test. The study conducted by Girod et al. (2010) for improvement of the length of hospital stay 207 for free of variance F. The difference in quality of life for two groups of gender was analysed using Mann Whitney test (Vladan et al., 2010). The study found that sex structure is the factors that affected the quality of life after CABG surgery.

3 PREDICTORS OF PROLONGED HOSPITAL STAY IN CARDIAC SURGERY METHODOLOGY Data and Statistical Analysis This study included 3560 patients who received Coronary Artery Bypass Graft surgery (CABG) in the treatment of semi-government hospital, Malaysia for three years ( ). Patient data comprised: the dates of operation and discharge from the hospital, birth, sex, type of medical operations (CABG and valve surgery and combination of both), major risk factors (diabetes melitus, hypertension and obesity) and other clinical conditions (stroke and wound infection). Patients underwent valve and combination surgery were excluded in this study. Data analysis was performed with SPSS version 17 software (SPSS from the operation date to the date of hospital discharge. Prolonged stay (Yaseen et al., 2002). Deaths were censored in the analysis. Kaplan-Meier method was used to estimate the survival curves. Log-Rank test was used to evaluate the difference between the curves. For p-value less than 0.10, data were compared by Mann-Whitney U test. 69

4 JOURNAL OF ASIAN BEHAVIOURAL STUDIES, VOLUME 2, NUMBER 7, OCTOBER 2012 RESULTS Univariate Analysis Table 1: Univariate Analysis of Patient Demographics and Postoperative factors Variable Gender Number of patients Normal Stay Prolonged stay p-value p-value (%) (mean ± SD) (logrank test) Median (Mann- Whitney test) male 2936(85.65%) 10.76± ± female 492(14.35%) 11.39± ± Age < (89.76%) 10.71± ± (10.24%) 12.13± ± Diabetes yes 1823(53.18%) 11.52± ± no 1605(46.82%) 10.09± ±1.445 Hypertension yes 2835(82.70%) 10.94± ± no 593(17.30%) 10.45± ±2.579 Obesity yes 80(2.33%) 12.21± ± no 3348(97.66%) 10.82± ±1.058 Stroke yes 9(%) 26.33± ± no 3419(99.73%) 10.81± ±5.51 Wound Infection yes 189(5.51%) 24.06± ± no 3239(94.49%) 10.08± ± Table 1 summarizes the univariate analysis of patients demographics and postoperative factors patients underwent CABG surgery survived and were discharged from hospital. Among this cohort 492 (14.35%) were female patients and 2936 (85.65%) male patients with the average of normal stay ± 0.15 days. The female patients stayed longer in the hospital compared to the male patients. The achieved results reported that for the

5 PREDICTORS OF PROLONGED HOSPITAL STAY IN CARDIAC SURGERY average of normal stay in the hospital, patients with diabetes, hypertension, stroke and wound infection stay longer compared to normal patients with the same risk factors. This is proven by Mann-Whitney test U which all the factors (gender, age, diabetes, hypertension, obesity, stroke and wound and wound infections tend to have prolonged hospitalization than patients Multivariate Analysis The analysis is further extended by dividing the study population according to gender categories of male and female patients (Table 2). Discussions began with a group of less than 70 years of age. Male group had the highest number of 2172 patients (74%) compared to 366 female patients who had hypertension and the difference is almost 7 times more than women. This trend is followed for other risk factors such as diabetes 1384 male patients (47.1%) than 266 female patients. The number of male patients for risk factors of obesity, stroke and wound infection were 4 to 7 times higher than female patients. Table 2: Multivariate Analysis of Patient Demographics and Postoperative Factors Gender Age Hypertension Diabetes Melitus Obesity Stroke Wound Infection No Yes No Yes No Yes No Yes No Yes Total Male < 70 Total % 16.4% 74.0% 43.3% 47.1% 88.4% 2.0% 90.1%.3% 86.0% 4.4% 90.4% Total % 1.6% 8.0% 5.1% 4.5% 9.5%.1% 9.6%.0% 8.7%.9% 9.6% Total Total % 18.0% 82.0% 48.4% 51.6% 97.9% 2.1% 99.7%.3% 94.8% 5.2% 100.0% 71

6 JOURNAL OF ASIAN BEHAVIOURAL STUDIES, VOLUME 2, NUMBER 7, OCTOBER 2012 Female < 70 Total % 11.6% 74.4% 31.9% 54.1% 82.3% 3.7% 86.0%.0% 79.7% 6.3% 86.0% Total % 1.6% 12.4% 5.5% 8.5% 13.8%.2% 13.8%.2% 13.2%.8% 14.0% Total Total % 13.2% 86.8% 37.4% 62.6% 96.1% 3.9% 99.8%.2% 92.9% 7.1% 100.0% Kaplan-Meier Analysis Figure 1: Survival Analysis for Each Gender 72 Figure 2: Survival Analysis for Two Age Groups

7 PREDICTORS OF PROLONGED HOSPITAL STAY IN CARDIAC SURGERY Figure 1 shows the survival curves for both gender groups who underwent CABG surgery. It was found that there are differences in survival between the two gender groups. The survival curves for both gender were almost identical at the beginning of the study (47 days). After 47 days, the survival curve for female patients is higher than the male curve. From 96 days until the end of the study, only the survival curves for male patients can be seen. One possible reason could be there is no longer female patients who stayed in the same period. The results obtained from the log-rank test where value of p is equal to until 39 days, the survival function for patients aged 70 years above and below were almost identical. Between 40 days and 55 days, patients with aged more than 70 years (4.8%) stayed shorter period in the hospital than patients aged less than 70 years (3.3%). There were 1 patient of aged > days. Log-rank test showed that the p-value is which is greater two age groups. As a conclusion, age is not a risk factor that cause patients to stay longer in the hospital. function for patients with diabetes was higher than the non-diabetic patients. This shows that the diabetic patients (61.14%) required prolonged stay than the non-diabetic patients (37.65%). From 90 to 194 days, only 2 patients for both categories received treatment in the hospital. We can conclude that there exists a difference for prolonged stay in hospital for both groups. It p-value = 0.059). 39 days of the study, the two functions were almost identical. From 40 to 194 days, a total of 32 patients with hypertension required longer period of stay in hospital. P stay existed between the two groups. This suggests that high blood pressure is not a risk factor affecting the long period stay in hospital. 73

8 JOURNAL OF ASIAN BEHAVIOURAL STUDIES, VOLUME 2, NUMBER 7, OCTOBER 2012 Figure 3: Survival Analysis for Diabetec and Non-Diabetic Patients Figure 4: Survival Analysis for Hypertensive and Non-Hypertensive Patients 74 The three-year Kaplan-Meier analysis (figure 5) estimates the survival estimations for non-obese (66.87%) and obese patients (4.21%) were identical. After 28 days until day-50, it was found that the non-obese stayed in the hospital in a shorter period of time compared to obese patients. The survival curves for patients with obesity can be seen until the end of the study. This could be due to there is no longer obese patients in the hospital during this period. P

9 PREDICTORS OF PROLONGED HOSPITAL STAY IN CARDIAC SURGERY The comparison of the prolonged stay in hospital between patients who experienced complication such as wound infection and patients without p-value 0.00). From beginning till the end of until day-60, patients without the stroke complications required prolonged stay in hospital than patients who had a stroke after CABG surgery. p-value = 0.269) of length of stay between the two groups. Figure 5: Survival Analysis for Obese and Non-Obese Patients Figure 6: Survival Analysis for Patients with and without Complication (Wound Infection) 75

10 JOURNAL OF ASIAN BEHAVIOURAL STUDIES, VOLUME 2, NUMBER 7, OCTOBER 2012 Figure 7 DISCUSSION Risk Factors for Prolonged Stay After CABG Surgery 76 Descriptive statistics showed that 50% of male and female patients were hospitalized more than 14 days as for all the risk factors such as obesity, hypertension, diabetes and complications. From previous study, obesity was often associated with factors of stay in hospitals (Paul et al., 2000). Age greater than 70 years is also a common factor for longer stay in hospital. This is evident from the Mann Whitney U test performed on cardiac patients in hospital. The average survival period of stay for comparison group of risk factors are similar to each other except for patients who had complications of wound infection. From the analysis, it is showed that male patients with stroke were more likely to have longer hospitalization period than female patients with stroke. This result is similar to Goto et al. (2007). Women is found to stay longer in the hospital compared to men (Paul et al., 2000). Research shows that most of U.S. CABG patients stay in hospital for 5 days (Eric et al., 2002). This is contradict to the study conducted by researcher where most of Asian CABG patients stay in hospital for 8 days. In the preliminary univariate analysis, most of the patients stayed in the hospital for more than 14 days is more susceptible to complications.

11 PREDICTORS OF PROLONGED HOSPITAL STAY IN CARDIAC SURGERY Perspective of Future Impacts Length of stay (LOS) factors (especially involving prolonged hospitalization) in hospital for patients undergone CABG has important roles in institution, health economic and clinical implication as it shows input and output system. For example, number of patients can be increased (output) due to availability number of beds (output) (Ine et al., 2008). This means that the hospital management will be able to optimize the usage of availability resources and patients do not have to wait for long periods of time to be admitted to the hospital. It is reported that 30% of patients need to wait for admission in the hospital (Rony et al., 2008). Therefore, time can be saved and the quality of services will be more effective. The increase of patients requires changes of management aspects, especially in surgery schedule and a daily of routine clinicals. Quality of Life From the analysis, it shows that the population of male patients 2936 (85.65)% is higher as compared to female patients 492 (14.35%). This might be due to the sensitivity of female patients to health care and by frequent consultation regarding their symptoms to health experts (Mechanic., 1976). It is believed that precaution taken by female patients have prevented them from experience decline in their health. Furthermore, it shows women had their therapeutic procedures effectively. This theory is the effects of biological and social factors (Vladan et al that female patients spent more time in a hospital than male patients. This is due to age factor as female patients (14%) underwent CABG at older age compared to male patients (9.6%). Older patients required more time for recovery process as compared to younger ones. Male patients (88.89%) are more likely to suffer from stroke after CABG surgery compared to female patients (11.11%). Aortic atherosclerosis increase the risk factor of stroke (Tomoko et al., 2007). Stroke directly proportional to high cholestrol level that corresponds to quality of life incuding lifestyle, daily diet and genetic factors. 77

12 JOURNAL OF ASIAN BEHAVIOURAL STUDIES, VOLUME 2, NUMBER 7, OCTOBER 2012 CONCLUSION Researcher faced with a few constraints when conducting this research. The variables used were very limited because the patients data were strictly has several problems such as the truncated data and the outcome variable is not normally distributed (Marubini, 2004). New statistical methods need to the hospital stay (gender, age, diabetes, hypertension, obesity, stroke and wound infection). Two independent factors such as diabetes and wound infection have been shown associated with a prolonged stay for the patients after surgery. Findings proven that longer stay after cardiac surgery can of these risk factors will help the health care institution to improve the quality of services while the community would realize the importance of healthy lifestyle. ACKNOWLEDGEMENT My heartiest gratitude to the Research Management Institute for the research grant awarded for this study and the semi-government hospital for providing data. REFERENCES Girod,A.,Brancati,A.,Mosseri,V.,,Kriegel,I.,Jouffroy,T., Rodriguez,J. of oral and pharyngeal cancer in the context of the new French casemixbased funding.oral.oncology 46(3): Goto, T., Baba,T. Ito, A., Maekawa, K., Koshiji, T. (2007). Gender Differences in Stroke Risk among the elderly after coronary artery surgery. Anesth Analg 104: Ine,B., Richard, H., Tijn, K., Gert, W.(2008). Benchmarking and Reducing Length of Stay in Dutch Hospitals. Biomed Central 8:

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